# -*- coding = utf8 -*-

import loaders.loader as ml
import tensorflow as tf

mnist = ml.get_mnist_set(True)

sess = tf.InteractiveSession()
x = tf.placeholder("float", shape=[None, 784])
y_ = tf.placeholder("float", shape=[None, 10])

W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))

sess.run(tf.global_variables_initializer())

saver = tf.train.Saver(tf.global_variables())

# The default is -1 which indicates the last dimension
# softmax = exp(logits) / reduce_sum(exp(logits), dim)

y = tf.nn.softmax(tf.matmul(x,W) + b)

cross_entropy = -tf.reduce_sum(y_*tf.log(y))

train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)


for i in range(1000):
    batch = mnist.train.next_batch(100)
    train_step.run(feed_dict={x: batch[0], y_: batch[1]})

correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))


model_dir = '/Users/vista/PycharmProjects/data/model/mnist/'
saver.save(sess, model_dir + 'm')
# if os.path.exists(model_dir + 'm.index'):
#     saver.restore(sess, model_dir + 'm')

print accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels})
